1. Technical Field
The present teaching relates to methods, systems, and programming for Internet services. Particularly, the present teaching relates to methods, systems, and programming for providing information to Internet users.
2. Discussion of Technical Background
Search engines are programs that search documents for specified keywords and return a list of the documents where the keywords were found. FIG. 1 illustrates a prior art search engine 100. The search engine 100 retrieves web pages by a web crawler. The contents of each page are then analyzed by a main module 102 to determine how it should be indexed. Data about web pages are stored in an index database 104 for use in later queries. When a user 106 enters a query 108 into the search engine 100 by using keywords, the main module 102 examines its index and provides the user 106 a listing of best-matching documents, e.g., web pages, from the index database 104 as query results 110 according to its criteria. However, the known search engine 100 only looks for the words or phrases in the documents exactly as entered from the query. It allows only query of documents through keywords. In other words, the query 108 is limited to only keywords, and the query result is limited to documents in the prior art search engine 100.
Personalized content recommendation may be available in the prior art search engine 100 by a content analyzer 112 and a content suggestion module 114. Traditional content recommendation may be realized in one of two ways—through collaborative filtering or content-based filtering. Collaborative filtering approaches build a model based on the user's public information, e.g., past behaviors, as well as similar decisions made by other users, and use that model to predict other content that the user may be interested in. Content-based filtering approaches utilize a series of discrete characteristics of known content stored in the database 104 in order to recommend additional content with similar properties. In addition, the prior art search engine 100 usually considers only explicit relationships among users and interests of users explicitly expressed based on their online content consumption activities. Implicit relationships, although handled in some existing technologies, are most identified via ad-hoc approaches. Furthermore, traditional recommendation systems usually only acquire data voluntarily provided by users, e.g., through questionnaires, or data recorded by the recommendation systems when users are directly interacting with the recommendation systems, e.g., cookies or activity logs when the users are signing in the recommendation systems. As a result, new users or inactive users of the recommendation systems cannot be used to provide data for building recommendation models. Accordingly, for new users or inactive users whose personal data is unavailable or sparse, the traditional systems become less effective in personalized content recommendation.
Therefore, there is a need to provide an improved solution for hybrid information query based on information associated with users, whether such information is static, dynamic, offline, online, explicit or implicit, all in a systematic and effective manner in order to solve the above-mentioned problems.